CKD risk prediction models achieve AUROC 1.00 internally but drop to 0.48-0.58 externally with high calibration error and low deployment scores, indicating need for external validation.
Reporting guideline for the early stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE- AI
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Calibration, Uncertainty Communication, and Deployment Readiness in CKD Risk Prediction: A Framework Evaluation Study
CKD risk prediction models achieve AUROC 1.00 internally but drop to 0.48-0.58 externally with high calibration error and low deployment scores, indicating need for external validation.